TITLE:
Intelligent Systems for Sun-Earth-Anthropogenic Climate Research: A Program for Causal, Hybrid, and Decision-Relevant Modeling
AUTHORS:
Marilia Hagen
KEYWORDS:
Sun-Earth Connections, Anthropogenic Disturbances, Climate Change, Causal Discovery, Physics-Informed Machine Learning, Neural Operators, Hybrid Modeling
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.13 No.12,
December
12,
2025
ABSTRACT: This research program enhances our understanding of how the Sun influences Earth, leading to cyclic global climate variations. The Sun exhibits many well-known features and events that consistently impact Earth. These solar phenomena directly affect the magnetosphere, which is the region of space around Earth shaped by its magnetic field. As a result of these interactions, changes occur in the atmospheric layers near the surface. Recognizing these links between solar activity, the magnetosphere, and atmospheric layers is essential for accurately understanding the processes behind climate variability and weather patterns. The influence of solar events on the lower atmosphere underscores the importance of integrating Sun-Earth interactions into climate research. Human impacts related to greenhouse gases are not entirely global; patterns of atmospheric and debris transport are mainly confined within the troposphere, stratosphere, and mesosphere, as well as by each hemisphere. Furthermore, these factors primarily restrict pollutants from circulating freely throughout Earth’s atmosphere. Human activities are adding organic and inorganic debris to land and oceans, altering the Earth’s crust. Densely populated areas are more heavily affected than less populated regions. Changes in atmospheric interactions, climate change, and global warming are likely to be more pronounced in densely populated regions and less so in areas with lower population density. The demand for vital services and resources—including food, sanitation, infrastructure, healthcare, housing, education, and leisure—is growing; consequently, emissions of various pollutants, such as methane and CO2, are expected to increase. The environmental degradation we observe today is worsened by various human-related factors, including intensive agriculture and livestock production, as well as the extraction of water and mineral resources. Human activities also lead to the release of plastics and other pollutants into the oceans, where ocean currents quickly spread materials that do not break down across the globe, causing persistent contamination because some substances biodegrade very slowly. This analysis is grounded in established principles of climate science and aims to be accessible across various disciplines, including environmental science, health, agriculture, economics, and policy. The conclusions highlight both immediate opportunities and long-term consequences that could emerge from the development of general-purpose intelligent systems, underscoring the importance of human-centered oversight, transparency, and equitable outcomes.